import torch
from torch import nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class PreNorm(nn.Module):
    def __init__(self, dim, fn, multimodality=False):
        super().__init__()
        self.multimodality = multimodality
        self.norm = nn.LayerNorm(dim)
        self.fn = fn
    def forward(self, x, **kwargs):
        if self.multimodality:
            x, y = x
            return self.fn((self.norm(x), self.norm(y)))
        return self.fn(self.norm(x), **kwargs)

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        return self.net(x)

class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., multimodality=False):
        super().__init__()
        self.multimodality = multimodality
        inner_dim = dim_head *  heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim = -1)
        if self.multimodality:
            self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
            self.to_q = nn.Linear(dim, inner_dim, bias = False)
        else:
            self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
        

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x):
        if self.multimodality:
            assert isinstance(x, tuple), "x must contain two tensor"
            x_kv, x_q = x
            k, v = self.to_kv(x_kv).chunk(2, dim = -1)
            q = self.to_q(x_q)
            qkv = (q, k, v)
            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)

        else:
            assert isinstance(x, torch.Tensor), "x must contain one tensor"
            qkv = self.to_qkv(x).chunk(3, dim = -1)
            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)

        out = torch.matmul(attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout, multimodality=False)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))
    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return x

class Cross_Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout, multimodality=True), multimodality=True),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))
    def forward(self, x, y):
        '''
        params x: k, v
        params y: q
        '''
        for attn, ff in self.layers:
            x = attn((x, y)) + x
            x = ff(x) + x
        return x